This study presents the dynamic model for an industrial Universal Oil Product (UOP) fluid catalytic cracking unit on basis of mass and energy balance and the transfer functions are found. The riser and regenerator are simulated by using Matlab/Simulink software. The dynamic behaviour of the process is carried out by measuring the temperature response of the riser and regenerator to step change in gas oil flow rate, gas oil temperature, catalyst flow rate, air flow rate and air temperature. The simulator is validated by comparing the steady-state behaviour of the system with those in the literature and plant data and the results are found to be in good agreement. The results of simulation show that the process is strongly influenced by the catalyst flow rate, and any increase in the amount of coke on the catalyst and regenerator temperature would be compensated by a larger rate of consumption of the oxygen.

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This paper deals with the multivariable control system of the distillation tower by applying multi-structures in MATLAB/Simulink for a binary mixture of benzene and toluene. Four structures configurations of the distillation are applied based on the level and temperature variables. The PID controller is used in all structures in multivariable. These structures are compared with different disturbances. The integral absolute error is a criterion to test the controller\\\’s performance under step change disturbances. The controller\\\’s performance was investigated by recording responses to disturbances in set-point of reflux ratio, flow-rate of top and bottom products. The step testing appears to be the single-ended temperature control with bottom and top-level structures to regulate the flow rate of the bottom and top products. The best structure is top-level, bottom level and condenser temperature because the column made more stable, the integral absolute error is minimum value and fast access to set-point value.

In this study the dynamic behavior for two control methods of the distillation column for the separation of methanol and toluene mixture are studied. The experimental responses of temperature in each tray of distillation column for step changes in set point of reboiler, reflux ratio and feed weight fraction were obtained. Based on a derived mathematical model, the Simulink simulator of the distillation column is used to implement the PID and fuzzy logic control methods. The Comparison between two controllers is done for step changes in set point, feed flow rate, feed weight fraction and liquid reflux. The controller performance is measured by using mean square error and integral square error. The results showed that the performance of the fuzzy controller is the best than the PID controller in fast access to the desired value and cancelling the disturbances.

A neural networks controller is developed and used to regulate the temperatures in a crude oil distillation unit. Two types of neural networks are used; neural networks predictive and nonlinear autoregressive moving average (NARMA-L2) controllers. The neural networks controller that is implemented in the neural network toolbox software uses a neural network model of a nonlinear plant to predict future plant performance. Artificial neural network in MATLAB simulator is used to model Baiji crude oil distillation unit based on data generated from aspen-HYSYS simulator. A comparison has been made between two methods to test the effectiveness and performance of the responses. The results show that a good improvement is achieved when the NARMA-L2 controller is used with maximum mean square error of 103.1 while the MSE of neural predictive is 182.7 respectively. Also shown priority of neural networks NARMA-L2 controller which gives less offset value and the temperature response reach the steady state value in less time with lower over-shoot compared with neural networks predictive controller.